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Register a new userToward a Calculus of Redundancy: The feedback arrow of expectations in knowledge-based systems
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Added by
Loet Leydesdorff
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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This paper considers the relationships among meaning generation, selection, and the dynamics of discourse from a variety of perspectives ranging from information theory and biology to sociology. Following Husserls idea of a horizon of meaning in intersubjective communication, we propose a way in which, using Shannons equations, the generation and selection of meanings from a horizon of possibilities can be considered probabilistically. The information-theoretical dynamics we articulate considers a process of meaning generation within cultural evolution: information is imbued with meaning, and through this process, the number of options for the selection of meaning in discourse proliferates. The redundancy of possible meanings contributes to a codification of expectations within the discourse. Unlike hard-wired DNA, the codes of non-biological systems can co-evolve with the variations. Spanning horizons of meaning, the codes structure the communications as selection environments that shape discourses. Discursive knowledge can be considered as meta-coded communication which enables us to translate among differently coded communications. The dynamics of discursive knowledge production can thus infuse the historical dynamics with a cultural evolution by adding options, that is, by increasing redundancy. A calculus of redundancy is presented as an indicator whereby these dynamics of discourse and meaning may be explored empirically.
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Knowledge Organization Systems (e.g. taxonomies and ontologies) continue to contribute benefits in the design of information systems by providing a shared conceptual underpinning for developers, users, and automated systems. However, the standard mechanisms for the management of KOSs changes are inadequate for systems built on top of thousands of data sources or with the involvement of hundreds of individuals. In this work, we review standard sources of change for KOSs (e.g. institutional shifts; standards cycles; cultural and political; distribution, etc) and then proceed to catalog new sources of change for KOSs ranging from massively cooperative development to always-on automated extraction systems. Finally, we reflect on what this means for the design and management of KOSs.
When studying the evolution of complex systems one refers to model representations comprising various descriptive parameters. There is hardly research where system evolution is described on the base of information flows in the system. The paper focuses on the link between the dynamics of information and system evolution. Information, exchanged between different systems parts, before being processed is first provided with meaning by the system. Meanings are generated from the perspective of hindsight, i.e. against the arrow of time. The same information can be differently interpreted by different systems parts (i,e,provided with different meanings) so that the number of options for possible system development is proliferated. Some options eventually turn into observable system states. So that system evolutionary dynamics can be considered as due to information processing within the system. This process is considered here in a model representation. The model under study is Triple Helix (TH) model, which was earlier used to describe interactions between university, industry and government to foster innovations. In TH model the system is comprised of three interacting parts where each part process information ina different way. The model is not limited to the sphere of innovation and can be used in a broader perspective. Here TH is conceptualized in the framework of three compertment model used to describe infectious disease. The paper demonstrates how the dynamics of information and meaning can be incorporated in the description of Covid-19 infectious propagation. The results show correspondence of model predictions with observable infection dynamics.
We develop a localisation theory for certain categories, yielding a 3-arrow calculus: Every morphism in the localisation is represented by a diagram of length 3, and two such diagrams represent the same morphism if and only if they can be embedded in a 3-by-3 diagram in an appropriate way. The methods to construct this localisation are similar to the Ore localisation for a 2-arrow calculus; in particular, we do not have to use zigzags of arbitrary length. Applications include the localisation of an arbitrary model category with respect to its weak equivalences as well as the localisation of its full subcategories of cofibrant, fibrant and bifibrant objects, giving the homotopy category in all four cases. In contrast to the approach of Dwyer, Hirschhorn, Kan and Smith, the model category under consideration does not need to admit functorial factorisations. Moreover, our method shows that the derived category of any abelian (or idempotent splitting exact) category admits a 3-arrow calculus if we localise the category of complexes instead of its homotopy category.
Understanding emerging areas of a multidisciplinary research field is crucial for researchers,policymakers and other stakeholders. For them a knowledge structure based on longitudinal bibliographic data can be an effective instrument. But with the vast amount of available online information it is often hard to understand the knowledge structure for data. In this paper, we present a novel approach for retrieving online bibliographic data and propose a framework for exploring knowledge structure. We also present several longitudinal analyses to interpret and visualize the last 20 years of published obesity research data.
Industrial knowledge is complex, difficult to formalize and very dynamic in reason of the continuous development of techniques and technologies. The verification of the validity of the knowledge base at the time of its elaboration is not sufficient. To be exploitable, this knowledge must then be able to be used under conditions (slightly) different from the conditions in which it was formalized. So, it becomes vital for the company to permanently evaluate the quality of the industrial knowledge implemented in the system. This evaluation is founded on the concept of robustness of the knowledge formalized by conceptual graphs. The evaluation method is supported by a computerized tool.
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